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Postman will conduct hands-on workshops where you will learn how to build agent-ready APIs 👉 Here's how to generate MCP servers in seconds from APIs using Postman. - Go to Postman MCP article: - Choose Create AI agents - Choose a Workspace or Collection and select your APIs. -... show more

Poonam Soni

219,321 subscribers

44,320 次观看 • 1 年前 •via X (Twitter)

健康养生科学技术教育

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Poonam Soni 的头像

Poonam Soni1 年前

I just built an MCP server without writing a single line of code. And the result is INSANE Here's the easiest way to build an MCP server:

Poonam Soni 的头像

Poonam Soni1 年前

I had a locally running API server of Instagram like application which I exposed as an MCP server and tested it using Postman client. You can seamlessly test these MCP tools right from the postman app on Desktop. Checkout the tutorial.

Poonam Soni 的头像

Poonam Soni1 年前

AI agents aren’t just calling a single API; they’re orchestrating multi-step, multi-service workflows. There are a lot of challanges in getting it right ! ✅ Discoverability ✅ Standardization ✅ Compatibility That’s where MCP (Model Context Protocol) steps in. It’s the missing layer that makes APIs truly agent-ready. With Postman you can create MCP server out of your or any public API collection in a single click.

Poonam Soni 的头像

Poonam Soni1 年前

Postman published their article on MCP and APIs that will standardize the Agent Revolution. MCP integration is right in Postman. You can build and test MCP servers literally in seconds. Read article here:

Poonam Soni 的头像

Poonam Soni1 年前

Postman is making APIs agent ready and to make it easy for everyone POST/CON 2025 is happening on.. 📍 June 3–4 | LA Live 🛠️There will be hands-on workshops where you will learn how to build agent-ready APIs 👉 Link to the article: Big Thanks to @getpostman for the collab.

Poonam Soni 的头像

Poonam Soni1 年前

Don't forget to bookmark for later. If you enjoyed reading this post, please support it with like/repost of the post below 👇

Greg Caplan 🚀 的头像

Greg Caplan 🚀2 年前

Stop wasting time following up with leads. Let our AI agents do it for you.

Anand Sheth / Consistently Deliver Top CX at Scale 的头像

Anand Sheth / Consistently Deliver Top CX at Scale1 年前

Why do all these companies fail to mention the "where"... As in an in person location is where again?

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I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

I'm still picking my jaw off the floor with this MCP update: Postman will now automatically build an MCP server for an API. Automatically! You select your API and the requests you want to include, and Potman will generate a Zip file with everything you need. In collaboration with Postman, I recorded a video showing how I generated an MCP server for the Google Maps API. It's glorious! Also, and before I forget: You can now test any MCP server with your Postman application. They built MCP right in, so now you can: • Connect to an MCP server • List out all of the available tools • Test any of the tools This is a 10x boost for the MCP ecosystem. If you've tried the default MCP debugger, you'll understand how huge a deal it is that you can do all of that from Postman now. Go here to give this a try:

Santiago

215,727 次观看 • 1 年前

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!

Today we're introducing Postman Fabric Gateway—an AI-native agent gateway. Here's why it matters: agents will consume APIs at several orders of magnitude beyond traditional apps. Traditional API Gateways are not designed for AI agents. While many legacy vendors have tried to support agentic workloads, Postman customers have been asking us about something that is AI-native and doesn’t have the baggage of pre-AI architecture. Postman Fabric has been designed from the ground-up for agents with support for MCPs, CLIs, as well as APIs - both AI and deterministic APIs. We are building this with a modular architecture as we are still in the early innings of AI and there are many new patterns that are likely to emerge. One thing is certain though, agents are driven by APIs, and you can’t have agent-ready APIs without a purpose-built gateway. Through Postman Fabric, customers will be able to: 1️⃣ Have a single plane to manage all MCP servers - public or private 2️⃣ Expose internal APIs as MCP tools 3️⃣ Centralize SaaS credentials 4️⃣ Govern agent traffic and run policies at a consumer, route, or service level 5️⃣ Deploy on cloud or in a hybrid model We are opening up early access soon - let me know if you are interested in the comments or a DM!

Abhinav Asthana

18,131 次观看 • 2 个月前

You can now give AI agents the ability to interact with ANY website. This new MCP server lets Claude, Cursor, or any AI agent navigate, click, fill forms, and extract data from sites without APIs. Just connect it and give it a URL + goal. Here’s how to set it up:

You can now give AI agents the ability to interact with ANY website. This new MCP server lets Claude, Cursor, or any AI agent navigate, click, fill forms, and extract data from sites without APIs. Just connect it and give it a URL + goal. Here’s how to set it up:

Alvaro Cintas

77,654 次观看 • 7 个月前

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

Shubham Saboo

79,784 次观看 • 1 年前

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,010 次观看 • 1 年前

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,594 次观看 • 1 年前

To use MCP in Cursor 1⃣Add an MCP in settings 2⃣In Composer, select "Agent Mode" 3⃣Cursor will run the MCP tool based on your prompt Testing more MCPs — updates and a guide coming soon. Follow me for updates Available MCP servers in comments 👇

To use MCP in Cursor 1⃣Add an MCP in settings 2⃣In Composer, select "Agent Mode" 3⃣Cursor will run the MCP tool based on your prompt Testing more MCPs — updates and a guide coming soon. Follow me for updates Available MCP servers in comments 👇

Melvin Vivas

68,919 次观看 • 1 年前

APIs weren’t built for AI agents. Orthogonal (YC W26) fixes that. Agents and developers get instant access to any API on our marketplace via a single MCP or SDK. Orthogonal handles your API keys and payments, so your agent doesn’t have to. Pay as you go. Congrats on the launch Christian Pickett, Bera Sogut

APIs weren’t built for AI agents. Orthogonal (YC W26) fixes that. Agents and developers get instant access to any API on our marketplace via a single MCP or SDK. Orthogonal handles your API keys and payments, so your agent doesn’t have to. Pay as you go. Congrats on the launch Christian Pickett, Bera Sogut

Y Combinator

38,047 次观看 • 5 个月前

Executor v1.2.1 is now out, with full Google Discover API support Google Workspace, Search Console, and 70+ other Google APIs all supported Connects to any agent via MCP, only adds 1k tokens to context thanks to code mode, keeps you in the loop through MCP elicitation

Executor v1.2.1 is now out, with full Google Discover API support Google Workspace, Search Console, and 70+ other Google APIs all supported Connects to any agent via MCP, only adds 1k tokens to context thanks to code mode, keeps you in the loop through MCP elicitation

Rhys

32,970 次观看 • 4 个月前

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,696 次观看 • 10 个月前

Introducing SperaxOS Plugin Store Browse & install plugins right from the chat. Choose from many of your favorite apps, plugins, and tools or bring your own custom MCP Servers. Watch how anyone can import any MCP Server into SperaxOS chat in under 1 minute.

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Sperax | SperaxOS

22,924 次观看 • 4 个月前

Supabase now has an official MCP Server! This means you can create, edit, and connect a Supabase project to your app from your AI IDE like Cursor or Windsurf! We will also be adding more features to the MCP server, such as Edge Functions and Storage, in the near future!

Supabase now has an official MCP Server! This means you can create, edit, and connect a Supabase project to your app from your AI IDE like Cursor or Windsurf! We will also be adding more features to the MCP server, such as Edge Functions and Storage, in the near future!

Tyler Shukert

85,127 次观看 • 1 年前

Supabase MCP server now supports creating and deploying Edge Functions! Refresh your MCP server and make sure you have the `deploy_edge_function` tool, and you are good to go! Just ask your AI tool to create an edge function or a backend, and it will create it for you!

Supabase MCP server now supports creating and deploying Edge Functions! Refresh your MCP server and make sure you have the `deploy_edge_function` tool, and you are good to go! Just ask your AI tool to create an edge function or a backend, and it will create it for you!

Tyler Shukert

28,997 次观看 • 1 年前

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

The easiest way to build an MCP Server using Google DeepMind Gemini 2.5 Pro and get started! 1. Use Gitingest to get all the code and docs from the FastMCP repo 2. Download the code into a txt file 3. Go to AI Studio, upload the file, define what kind of MCP Server you want to build 4. Gemini 2.5 Pro builds it for you.

Philipp Schmid

75,968 次观看 • 1 年前

Introducing the official Supabase MCP Server! Supabase MCP Server allows you to read and write to your database, create new projects, apply migrations, and more, right from your favorite AI tools! You can start using them in Cursor or Windsurf today!

Introducing the official Supabase MCP Server! Supabase MCP Server allows you to read and write to your database, create new projects, apply migrations, and more, right from your favorite AI tools! You can start using them in Cursor or Windsurf today!

Supabase

193,141 次观看 • 1 年前

Kling MCP Tutorial: Create Cinematic AI Performances in Minutes Learn how to use Kling MCP to build a complete creative workflow in your favourite agent — from character creation and emotion design to batch video generation. Your next story starts here.

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Kling AI

1,046,709 次观看 • 3 天前

How to set up Sentry MCP server with Warp 👉Open MCP Server > Add Server 👉Paste in the Sentry MCP JSON 👉Press "Start" and go through authentication process 👉Copy the Sentry link you want to fix 👉Ask Agent Mode to fix the issue

How to set up Sentry MCP server with Warp 👉Open MCP Server > Add Server 👉Paste in the Sentry MCP JSON 👉Press "Start" and go through authentication process 👉Copy the Sentry link you want to fix 👉Ask Agent Mode to fix the issue

Warp

11,770 次观看 • 1 年前

🤖 Introducing our Model Context Protocol (MCP) Server The MCP protocol standardizes how apps provide context to LLMs. No more training LLMs — just connect them directly to our blockchain APIs for live token prices, transfers, NFT data, and more 💪 Build smarter AI agents today! Let’s gooooooo 🔗 -------------- TIMESTAMPS 0:00 Setting up the configuration file 0:30 Add configuration to Cursor 1:06 Executing prompts for the price of ETH 2:25 Getting data on Vitalik’s transfers 3:45 Contributing or accessing our MCP server

🤖 Introducing our Model Context Protocol (MCP) Server The MCP protocol standardizes how apps provide context to LLMs. No more training LLMs — just connect them directly to our blockchain APIs for live token prices, transfers, NFT data, and more 💪 Build smarter AI agents today! Let’s gooooooo 🔗 -------------- TIMESTAMPS 0:00 Setting up the configuration file 0:30 Add configuration to Cursor 1:06 Executing prompts for the price of ETH 2:25 Getting data on Vitalik’s transfers 3:45 Contributing or accessing our MCP server

Alchemy

11,523 次观看 • 1 年前